GoKawiilThe other day I was idly wondering what are the strangest combinations of items people buy at grocery stores. The kind of shopping cart that makes the cashier snicker and later tell his friends, "Dude, can you believe this guy came in and only bought condoms and apples?"
So I fired up Claude and started looking for any receipt data I could find.
For the final results, scroll to the bottom. Otherwise, read on to follow the journey.
Grocery stores keep this kind of data very close to the chest. There are consumer apps that collect receipt data (like ReceiptHog and Fetch) but they presumably just sell it to hedge funds or something. Years ago, however, Instacart open-sourced data from 3 million orders as part of a machine learning competition to optimize their recommendation algorithm. The data is still available on Kaggle and it's very rich. What we want is the exact opposite of a recommendation algorithm but this data should work fine.
The Instacart data set includes the following:
3,214,874 orders
orders ~10 products per order on average
products per order on average 49,688 unique products
unique products 134 unique "aisles" (product categories)
So all we have to do is look at every cart and see which combinations of items are least likely to appear, right? Let's try that. For the sake of testing a few groupings, I included pairs, triples, and quads.
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